Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments

Abstract

Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse-reward settings. Furthermore, a reactive avoidance reward function based on velocity-obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic environments. Experiments are conducted in a semi-structured dynamic crowd scenario constructed on the GAZEBO simulation platform. The results demonstrate that PPO-ICM-Attn achieves significant improvements in key metrics such as path success rate, travel time, and path efficiency compared to baseline methods like A*+DWA and standard DRL. Although the gap remains in path efficiency compared to A*+DWA, the proposed method exhibits superior robustness and navigation performance overall, validating its effectiveness in complex dynamic environments.